预测中风相关肺炎的机器学习模型:系统性审查和元分析
Bardia Hajikarimloo1, Ibrahim Mohammadzadeh2, Salem M Tos3
1Department of Neurological Surgery, University of Virginia, Charlottesville, VA, USA. bardii47@yahoo.com.
机器学习模型在预测中风相关肺炎 (SAP) 方面表现有希望,这是一个常见的并发症. 这些模型可以帮助早期识别高风险患者,但需要进一步验证临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床预测模型临床预测模型
背景情况:
- 脑卒中相关性肺炎 (SAP) 是中风后的一种严重并发症.
- 机器学习 (ML) 模型越来越多地被开发用于SAP预测.
研究的目的:
- 为SAP系统地评估ML,深度学习 (DL) 和神经网络 (NN) 模型的预测性能.
- 为这些预测模型提供聚合的绩效指标.
主要方法:
- 在PubMed,Embase,Scopus和Web of Science的系统文献搜索.
- 对27项使用R计算聚合AUC,准确性,敏感性,特异性和DOR的研究进行元分析.
- 对模型类型 (ML,DL,NN) 和输入数据 (临床,成像等) 的分析. ) 的情况.
主要成果:
- 聚合的AUC为0.84和聚合的准确度为0.80,表明强大的预测性能.
- 聚合灵敏度为0.73和特异性为0.85.
- 主要使用临床数据的ML模型显示出有希望的结果,但缺血性和出血性中风子组之间没有显著差异.
结论:
- 在临床实践中,基于ML的模型显示了SAP风险早期识别的巨大潜力.
- 进一步的外部验证和整合到临床工作流程中是广泛采用的必要条件.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
相关概念视频
Simplified Synchronous Machine Model
In this model, each generator is connected to a...
Wind Turbine Machine Models
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Review and Preview
Percentiles are a type of fractile that partition data into...
Review and Preview
Pneumonia I: Introduction
Risk Factors
Various factors influence the likelihood of developing pneumonia. Age plays a crucial role, with infants, children under two, and individuals over 65 at increased risk due to their...
Pneumonia II: Pathophysiology
